Total Aboveground Biomass Research Articles

Boosting photosynthesis opens new opportunities for agriculture sustainability and circular economy: TheBEST-CROP research and innovation action.

There is a need for ground-breaking technologies to boost crop yield, both grains and biomass, and their processing into economically competitive materials. Novel cereals with enhanced photosynthesis and assimilation of greenhouse gasses, such as carbon dioxide and ozone, and tailored straw suitable for industrial manufacturing, open a new perspective for the circular economy. Here we describe the vision, strategies, and objectives of BEST-CROP, a Horizon-Europe and United Kingdom Research and Innovation (UKRI) funded project that relies on an alliance of academic plant scientists teaming up with plant breeding companies and straw processing companies to use the major advances in photosynthetic knowledge to improve barley biomass and to exploit the variability of barley straw quality and composition. We adopt the most promising strategies to improve the photosynthetic properties and ozone assimilation capacity of barley: (i) tuning leaf chlorophyll content and modifying canopy architecture; (ii) increasing the kinetics of photosynthetic responses to changes in irradiance; (iii) introducing photorespiration bypasses; (iv) modulating stomatal opening, thus increasing the rate of carbon dioxide fixation and ozone assimilation. We expect that by improving our targeted traits we will achieve increases in aboveground total biomass production without modification of the harvest index, with added benefits in sustainability via better resource-use efficiency of water and nitrogen. In parallel, the resulting barley straw is tailored to: (i) increase straw protein content to make it suitable for the development of alternative biolubricants and feed sources; (ii) control cellulose/lignin contents and lignin properties to develop straw-based construction panels and polymer composites. Overall, by exploiting natural- and induced-genetic variability as well as gene editing and transgenic engineering, BEST-CROP will lead to multi-purpose next generation barley cultivars supporting sustainable agriculture and capable of straw-based applications.

Improved aboveground biomass estimation and regional assessment with aerial lidar in California’s subalpine forests

BackgroundUnderstanding the impacts of climate change on forest aboveground biomass is a high priority for land managers. High elevation subalpine forests provide many important ecosystem services, including carbon sequestration, and are vulnerable to climate change, which has altered forest structure and disturbance regimes. Although large, regional studies have advanced aboveground biomass mapping with satellite data, typically using a general approach broadly calibrated or trained with available field data, it is unclear how well these models work in less prevalent and highly heterogeneous forest types such as the subalpine. Monitoring biomass using methods that model uncertainty at multiple scales is critical to ensure that local relationships between biomass and input variables are retained. Forest structure metrics from lidar are particularly valuable alongside field data for mapping aboveground biomass, due to their high correlation with biomass.ResultsWe estimated aboveground woody biomass of live and dead trees and uncertainty at 30 m resolution in subalpine forests of the Sierra Nevada, California, from aerial lidar data in combination with a collection of field inventory data, using a Bayesian geostatistical model. The ten-fold cross-validation resulted in excellent model calibration of our subalpine-specific model (94.7% of measured plot biomass within the predicted 95% credible interval). When evaluated against two commonly referenced regional estimates based on Landsat optical imagery, root mean square error, relative standard error, and bias of our estimations were substantially lower, demonstrating the benefits of local modeling for subalpine forests. We mapped AGB over four management units in the Sierra Nevada and found variable biomass density ranging from 92.4 to 199.2 Mg/ha across these management units, highlighting the importance of high quality, local field and remote sensing data.ConclusionsBy applying a relatively new Bayesian geostatistical modeling method to a novel forest type, our study produced the most accurate and precise aboveground biomass estimates to date for Sierra Nevada subalpine forests at 30 m pixel and management unit scales. Our estimates of total aboveground biomass within the management units had low uncertainty and can be used effectively in carbon accounting and carbon trading markets.

Using Drones for Dendrometric Estimations in Forests: A Bibliometric Analysis

Traditional field inventories have been the standard method for collecting detailed forest attribute data. However, these methods are often time-consuming, labor-intensive, and costly, especially for large areas. In contrast, remote sensing technologies, such as unmanned aerial vehicles (UAVs), have become viable alternatives for collecting forest structure data, providing high-resolution images, precision, and the ability to use various sensors. To explore this trend, a bibliometric review was conducted using the Scopus database to examine the evolution of scientific publications and assess the current state of research on using UAVs to estimate dendrometric variables in forest ecosystems. A total of 454 studies were identified, with 199 meeting the established inclusion criteria for further analysis. The findings indicated that China and the United States are the leading contributors to this research domain, with a notable increase in journal publications over the past five years. The predominant focus has been on planted forests, particularly utilizing RGB sensors attached to UAVs for variable estimation. The primary variables assessed using UAV technology include total tree height, DBH, above-ground biomass, and canopy area. Consequently, this review has highlighted the most influential studies in the field, establishing a foundation for future research directions.

Exogenous Riboflavin Application at Different Growth Stages Regulates Photosynthetic Accumulation and Grain Yield in Fragrant Rice

Fragrant rice has high market value and is popular among consumers because of its pleasant fragrance. Plant growth regulators and trace elements can influence the yield and physiological indices of fragrant rice. Riboflavin, a vital component of plant vitamins, plays a crucial role in plant growth and development, and it is a water-soluble vitamin that is essential for sustaining normal photosynthesis and metabolic processes. However, the effects of riboflavin on nutrient accumulation and yield formation in fragrant rice have rarely been reported. Therefore, to further increase the yield and quality of fragrant rice, this study investigated the impact of the foliar application of riboflavin at different growth stages on nutrient accumulation and yield formation in two different genotypes of fragrant rice via a pot experiment. The experimental design consisted of three treatment groups, i.e., the T0 treatment group (control), which was sprayed with water; the T1 treatment group, which was sprayed at the booting stage; and the T2 treatment group, which was sprayed at the flowering stage; in all of these groups, 20 mg·L−1 riboflavin solution was used. The results revealed that the yields of the T1 and T2 treatments increased by 6.56–7.25% and 10.52–13.80%, respectively, compared with those of T0, which was attributed mainly to the increase in 1000-grain weight and grain filling. Furthermore, foliar spraying of riboflavin significantly increased the chlorophyll content, which resulted in the increased production of more total starch, soluble sugars, and sucrose and facilitated their transportation to the seeds. Moreover, applying riboflavin directly to the leaves increased the activity levels of the sucrose synthase (SS) and sucrose phosphate synthase (SPS) enzymes. Among the three treatments, the T2 treatment had the most pronounced effect. The results revealed that the foliar application of riboflavin could increase photosynthesis and promote the production of nonstructural carbohydrates, thereby increasing the total aboveground biomass of fragrant rice by 17.00–20.91% and 21.07–72.91% under the T1 and T2 treatments, respectively. Additionally, spraying riboflavin promoted rapid increases in the weight of fragrant rice seeds. In conclusion, foliar spraying of riboflavin during the flowering stage of aromatic rice can effectively enhance photosynthetic accumulation and yield, which is a promising physiological regulation strategy and provides new theoretical guidance for high-yield cultivation practices.

Short‐term survival and growth of 32 native boreal plants on treated oil sands tailings

AbstractThe consolidation of oil sands tailings is a cost‐ and time‐consuming process that requires treatment via active (e.g., centrifugation) and passive (e.g., self‐weight consolidation) methods. The use of plants to dewater tailings is a promising concept and has previously been evaluated using agronomic grass species in greenhouse studies. This greenhouse study evaluated the short‐term survivorship and growth of 32 upland and lowland native plant species (12 forbs, 14 graminoids, and six woody plants) in centrifuged tailings and benchmarked their performance against reclamation soil and undisturbed forest soil. All plant species were propagated from seed and transplanted as seedlings into containers filled with one of the three substrates. After 42 days, the height (woody species only) and total aboveground biomass were determined for all living plants. As expected, the mortality of seedlings in tailings was higher than plants grown in the other two substrates. Graminoid species, regardless of species community type (wetland or upland), had higher survival probabilities and growth compared to forb or woody species across all substrates. Of forbs and woody species evaluated, Geum aleppicum and Populus tremuloides showed the most promise amongst the upland species, and Rumex occidentalis was the wetland equivalent.

Illuminating Cannabis sativa L.: The Power of Light in Enhancing C. sativa Growth and Secondary Metabolite Production.

Light is crucial for higher plants, driving photosynthesis and serving as a powerful sensory signal that profoundly modulates growth, development, physiological functions, hormone activation, and biochemical pathways. Various light parameters-quality, intensity, composition, and photoperiod-exert a tremendous influence on plant growth and development, particularly in industrial hemp (Cannabis sativa L.). C. sativa, a crop of historical significance and unparalleled versatility, holds immense value in the food, fiber, and medicinal industries. The cultivation of medicinal cannabis is burgeoning in controlled environments due to evolving healthcare regulations. Optimal light conditions significantly enhance both yield and harvest quality, notably increasing the density of apical inflorescences and the ratio of inflorescence to total aboveground biomass. C. sativa metabolites, especially phenolic and terpene compounds and Phytocannabinoids like CBD (cannabidiol), THC (tetrahydrocannabinol), and CBG (cannabigerol), possess immense medicinal value. Secondary metabolites in C. sativa predominantly accumulate in the trichomes of female flowers and surrounding sugar leaves, underscoring the critical need to boost inflorescence weight and metabolite concentrations while ensuring product consistency. Different light parameters distinctly impact C. sativa's metabolic profile, providing a robust foundation for understanding the optimal conditions for synthesizing specific secondary metabolites. While the effects of light measurement on various crops are well-established, scientific evidence specifically relating to light quality effects on C. sativa morphology and secondary metabolite accumulation remains scarce. In this review, we critically summarized how different light properties can alter cannabis growth (vegetative and reproductive), physiology and metabolism. Furthermore, the mechanisms by which specific wavelengths influence growth, development, and secondary metabolite biosynthesis in C. sativa are not fully elucidated, which could be a prospective task for future researchers. Our review paves the way for a profound understanding of light's influence on C. sativa growth and advancements in greenhouse settings to maximize metabolite production for commercial use.

Effects of polyester microplastics and naproxen on lettuce growth and development and soil abiotic factors

AbstractMicroplastics (MPs), formed from the physical breakdown of larger plastics, have been found across a variety of ecosystems. Much research has been done on the effects of MPs in aquatic ecosystems, but fewer studies have explored their effects in terrestrial environments, particularly in agroecosystems where modern practices contribute strongly to MP soil pollution (e.g., plasticulture, fertilization with contaminated biosolids, and composts). Aquatic pharmaceutical contamination is also considered an emerging pollutant threat. Naproxen, a commonly used drug, has been found in wastewater and natural freshwater bodies globally. Thus, crops may be exposed to multiple pollutants simultaneously through soil and water inputs. Our research provides insight into the individual and interactive effects of soil MP pollution intensity and exposure to naproxen‐contaminated water on growth and development of Lactuca sativa (lettuce). Soil abiotic factors (pH, electrical conductivity, and rates of soil moisture loss) are also reported. We found that 7% polyester MP‐contaminated soil significantly reduced total aboveground biomass and coarse root biomass, while both 0.03% and 7% polyester MP‐contaminated soil significantly reduced leaf count and average leaf size. However, 1 mg/L naproxen had no effect on plant growth or development, and there were no interactive effects of naproxen with MPs. MPs also significantly increased rates of soil moisture loss, but we found no effects of MPs or naproxen on soil pH or electrical conductivity. Predicted global increases in soil MP contamination levels will potentially have negative consequences for food systems.

Continuous Monitoring of Forests in Wetland Ecosystems with Remote Sensing and Probability Sampling

With the drastic reduction in wetland areas, it is essential to conduct an annual monitoring of the biomass or carbon content of wetland ecosystems to support international initiatives and agreements focused on sustainable development, climate change, and carbon equity. Forests in wetland ecosystems play a crucial role in carbon sequestration; however, the monitoring of small, fragmented forest components in wetlands remains insufficient, leading to an underestimation of their ecological and carbon sequestration functions. This study utilizes a model-assisted (MA) estimator, a monitoring procedure that is asymptotically design-unbiased and incorporates remote sensing, to assess the status and trends in the above-ground biomass (AGB) of forest components in wetlands, while also proposing a method of optimizing the sample size to enable continuous monitoring. Based on the population of the forest component of Baiyangdian wetland, major findings indicate that: (1) neglecting the forest component of Baiyangdian wetland will lead to an underestimation of the total aboveground biomass by 224.34 t/ha and 243.64 t/ha in the years 2022 and 2023, respectively; (2) in either year-specific monitoring or interannual change monitoring, the MA estimator is more cost-effective than the expansion estimator, a comparable procedure that relies solely on field observations; (3) the method used to optimize sample size can effectively tackle the cost-related concerns of subsequent continuous monitoring. Overall, the neglect of forest components is inevitably bound to give rise to an underestimation of wetlands, and use of an MA estimator and optimizing the sample size could effectively address the cost issue in continuous monitoring. This holds significant importance when developing management strategies to prevent the further degradation of wetland ecological functions and carbon sink capabilities.

Monitoring seagrass meadows in Maputo Bay using integrated remote sensing techniques and machine learning

Seagrass meadows are one of the most productive and valuable ecosystems on the planet. Monitoring seagrass meadows is essential to understand how these habitats change, and to develop better management and conservation practices. This study integrated satellite imagery from Sentinel-2 and Unmanned Aerial Vehicles (UAV) using machine learning to provide a consistent classification approach for monitoring seagrass in Maputo Bay, southern Mozambique. Sentinel-2 imagery was used to map seagrass extent and changes in Maputo Bay. The UAV systems were used to map seagrass at species level and biomass. All three algorithms tested in the ArcGIS environment could detect seagrass with high producer accuracy and Kappa coefficient. The area of seagrass in Maputo Bay decreased by 33.4 % between 1991 and 2023, with a decreasing trend of 0.48 km2/yr. A zonation pattern was observed for Oceana serrulata and Zostera capensis from the UAV imagery. The small and narrow leaved species (Z. capensis) occurred in the intertidal zone replaced by the broadleaved species (O. serrulata) in the subtidal. The total average aboveground biomass was 33.2 kg dry weight for the mapped area. The results of this study will guide implementation of combined satellite and UAV imagery with machine learning techniques for seagrass monitoring and restoration in Mozambique.

Monitoring aboveground organs biomass of wheat and maize: A novel model combining ensemble learning and allometric theory

Accurate monitoring of crop organ biomass facilitates optimizing agronomic strategies to maximize yield or economic benefit. Unmanned aerial vehicle (UAV) is extensively employed for aboveground biomass (AGB) monitoring at the farm scale, but previous studies have mostly concentrated on total AGB rather than individual organ biomass. Furthermore, film-mulched crops, a widely used cropping pattern in northwest China, have received less attention for AGB monitoring. We aim to develop a novel model to precisely estimate the AGB of leaf (AGBLeaf), stem (AGBStem), and reproductive organs (AGBR) by UAV for film-mulched wheat and maize. The maize-wheat rotation field experiments with treatments of five nitrogen application amounts and three planting densities were conducted from 2021 to 2023, respectively. Firstly, we constructed allometric models at jointing, heading (tasseling), and grain filling stages by ground sampling data in 2021–2022. Next, the input feature set was obtained by feature selection methods (Lasso and Boruta) using UAV image data, and three traditional methods (partial least squares, ridge regression, and support vector machine) and three ensemble learning models (random forest, extreme gradient boosting, and local cascade ensemble (LCE)) were trained for AGBLeaf inversion based on the physically-based PROSAIL model simulation dataset. Finally, the optimal AGBLeaf inversion hybrid model was coupled with the allometric model to estimate the AGBStem and AGBR in 2022–2023. The results indicated that both wheat and maize organ biomass conformed to the allometric pattern. While feature selection helped reduce computation and complexity, but didn’t improve monitoring accuracy. The normalized root mean square error (NRMSE) of the optimal hybrid model (PROSAIL + Boruta + LCE) on the measured wheat and maize AGBLeaf datasets were 12.72 %–24.93 % and 19.65 %–25.16 %, respectively. After coupling the allometric model, the coefficient of determination (R2) of wheat and maize AGBStem were 0.64–0.85 and 0.63–0.68, and the NRMSE were 15.05 %–25.28 % and 24.10 %–27.06 %, respectively; and the corresponding R2 of AGBR was 0.67–0.76 and 0.72, and the NRMSE were 16.81 %–22.12 % and 21.66 %, for wheat and maize, respectively. Overall, the novel model performed well in film-mulched wheat and maize, providing a cost-effective approach for organ biomass monitoring. In the future, further validation of the model’s transferability is necessary to increase the potential for generalization in production practice.

Plant growth and physiological responses of the invasive plant Acmella radicans to contrasting light and soil water conditions

Acmella radicans (Jacquin) R.K. Jansen is a new invasive species record for Yunnan Province, China, as of 2017 and little is known about its invasion mechanisms. To better understand its invasive strategies, we investigated the growth, physiological and soil nutrient use parameters of the invader under combined conditions of light (25%, 50%, 75%, and 100% of light availability) and soil water content (full, high, medium, and low soil water content) in the glasshouse. The results showed that light level, soil water content and their interaction had a significant effect on all plant morphological, physiological and soil nutrient parameters for A. radicans (P < 0.05). For the most part, plant height, total branch length, leafstalk length, leaf area, inflorescence number, seed number, leaf biomass, stem biomass, aboveground biomass, and total biomass of A. radicans were significantly increased with increased shading rate and soil water content and were generally greater at intermediate light levels and intermediate to high soil water levels. Under high-irradiance conditions, the chlorophyll content was greatly reduced compared to other treatments. We also observed that leafstalk length, leaf area, Pn, chlorophyll a, and chlorophyll b of A. radicans were markedly increased with increased shading rate and soil water content, and were generally highest under intermediate irradiance and soil water conditions, but many plant physiological parameters also exhibited relatively high values under waterlogging conditions. The concentrations of organic matter, available N and available K of A. radicans soils at high-irradiance and full-high soil water content treatments and medium-irradiance and high-medium soil water content treatments were often less than those of other treatments indicated that hypothetically soil absorption was increased by moderate shading and high soil water. This was the first study demonstrating that A. radicans thrives best under moderate light conditions in combination with high soil water. Furthermore, we surmised that its higher phenotypic and physiological plasticity contributes to its invasion success.

Topography and life stage regulate species aboveground biomass distribution in combination in a tropical montane rainforest.

Understanding the correlation between topography, species biomass and species life stage would allow forest managers to better foster carbon storage in forests. Using census data from a 60-ha plot in south China, we first quantified aboveground biomass (AGB) and how much it varied among different topographies. Next, the specific contribution of 42 dominant species to total aboveground biomass was analyzed for each of the different topographies. We also explored whether these species-topography associations changed, in terms of species' AGB distribution, during each of the three life stages (sapling, juvenile, adult) for these 42 species. Our results showed that the average AGB was 368.79 Mg ha-1 and that it varied noticeably among the four topographies (Low valley, Slope, High valley and Ridge, which were classified by using fuzzy C-mean clustering based on elevation, convexity, and slope). AGB was significantly lower in the two valleys than in the two other topographies. Of the 42 species, 88.1% showed topographic preferences, and 78.6% appeared to exhibit topographic preferences that changed with life stage. Our work highlights the importance of topography and life stage in species biomass distribution and suggests that different combinations of species and life stages, based on species topographic preferences across life stages, may be better suited in different tropical rainforest topographies to maximize carbon storage overall.

Development of site-specific allometric equation and predicting aboveground biomass of natural and plantation forests of Oxytenathera abyssinica (A. Rich.) Munro, Northwestern Ethiopia

Bamboo is an ideal plant for commercial production due to its rapid growth rate, high biomass production, low cost of production and environmental friendliness. Although Ethiopia has the highest bamboo cover in Africa, allometric equations for estimating its biomass are scarce. Most allometric models developed to date have been largely concerned with trees and shrubs. The objective of this study was to generate species- and site-specific allometric models that could be used to estimate the total aboveground dry biomass and culm dry biomass of lowland bamboo in northwest Ethiopia. Three power form-based allometric models were created using diameter at breast height (DBH) and culm height (H) as independent variables. One hundred and eight Oxytenathera abyssinica culms were used to predict the total aboveground biomass and culm biomass. Model one (M1) was the best model to predict the culm and total aboveground biomass of the species, regardless of forest type. The allometric models may provide useful information about aboveground biomass and culm biomass estimation methods to forestry professionals, bamboo producers and other stakeholders, and could help in the calculation of the country’s contribution to global carbon sequestration and trade.

Functional attributes of conifers expanding into temperate semi-arid grasslands modulate carbon and nitrogen fluxes in response to prescribed fire

Fire exclusion is a key factor driving conifer expansion into temperate semi-arid grasslands. However, it remains unclear how reintroducing fire affects the aboveground storage of carbon (C) and nitrogen (N) in the expanding tree species and belowground in soils. To assess the impacts of fire reintroduction C and N pools and fluxes in areas of conifer expansion we targeted a region of the Northern Great Plains that has experienced extensive woody plant expansion (WPE) of two species: ponderosa pine (Pinus ponderosa) and juniper (Juniperus spp). We quantified tree mortality of both species to estimate the amount of dead biomass C and N produced by a recent prescribed fire, in addition to changes in soil C, pyrogenic C (PyC), and N concentrations across a woody-cover gradient using a before/after/control experimental design. Post-fire soil chemical analysis revealed a 2 year increase in mineral soil C, PyC and N, suggesting the return of fire led to the transfer of partially combusted plant organic matter back to the soil. Further, we found that functional trait differences between the two species influenced the distribution of living conifer biomass-N prior to fire. Despite junipers having 41% less total aboveground biomass than ponderosa, they contained two times more aboveground N. Prescribed fire resulted in 88% mortality of all mature juniper stems and increased fire severity correlated with greater pre-fire juniper cover. Ponderosa mortality varied by size class, with > 40 cm stem diameter class having only 28% mortality. High mortality and greater aboveground N storage in juniper biomass, compared to ponderosa, led to 77% of the total conifer biomass N lost. Consequently, the functional attributes of expanding trees differentially contribute to fluxes of C and N after the return of fire, with junipers acting as conduits for N movement due to their relatively higher N content in less fire-resistant tissues and ponderosa serving as important and more stable storage pools for C. Together, these findings highlight the importance of considering species-specific traits when planning WPE management strrategies at landscape-scales, particularly when goals include C storage or soil nutrient status.Graphical abstractThe hypothesized effects of how carbon (C) and nitrogen (N) fluxes respond to the return of fire in grasslands experiencing conifer expansion. The question marks represent the input of fire-altered biomass from conifer trees into the soil and the transformation of living to dead tree biomass which are the focus of this study.

Constructing and Validating Estimation Models for Individual-Tree Aboveground Biomass of Salix suchowensis in China

Biomass serves as a crucial indicator of plant productivity, and the development of biomass models has become an efficient way for estimating tree biomass production rapidly and accurately. This study aimed to develop a rapid and accurate model to estimate the individual aboveground biomass of Salix suchowensis. Growth parameters, including plant height (PH), ground diameter (GD), number of first branches (NFB), number of second branches (NSB) and aboveground fresh biomass weight (FW), were measured from 892 destructive sample trees. Correlation analysis indicated that GD had higher positive correlations with FW than PH, NFB and NSB. According to the biological features and field observations of S. suchowensis, the samples were classified into three categories: single-stemmed type, first-branched type and second-branched type. Based on the field measurement data, regression models were constructed separately between FW and each growth trait (PH, GD, NFB and NSB) using linear and nonlinear regression functions (linear, exponential and power). Then, multiple power regression and multiple linear regression were conducted to estimate the fresh biomass of three types of S. suchowensis. For the single-stemmed plant type, model M1 with GD as the single parameter had the highest adj R2, outperforming the other models. Among the 16 constructed biomass-estimating equations for the first-branched plant type, model M32 FW = 0.010371 × PH1.15862 × GD1.250581 × NFB0.190707 was found to have the best fit, with the highest coefficient of determination (adj R2 = 0.6627) and lowest Akaike Information Criterion (AIC = 5997.3081). When it comes to the second-branched plant type, the best-fitting equation was proved to be the multiple power model M43 with PH, GD, NFB and NSB as parameters, which had the highest adj R2 value and best-fitting effect. The stability and reliability of the models were confirmed by the F-test, repeated k-fold cross-validation and paired-sample t-tests. The models developed in this study could provide efficient tools for accurately estimating the total aboveground biomass for S. suchowensis at individual tree levels. The results of this study could also be useful for optimizing the economic productivity of shrub willow plantations.

Effects of drought and moisture stress on the growth and ecophysiological traits of Schima superba seedlings.

Changes in rainfall patterns are important environmental factors affecting plant growth, especially when larger precipitation events and prolonged drought periods occur in subtropical regions. There are many studies on how drought reduces plant biomass through drought-sensitive functional traits, but how excess water affects plant growth and ecophysiology is still poorly understood. Therefore, a greenhouse experiment was conducted on Schima superba (Theaceae), a dominant tree species in subtropical forests and commonly used in forestry, in a closed chamber under control (25% soil water content (SWC) as in local forests), drought stress (D, 15% SWC) and moisture stress (W, 35% SWC). Plant growth and ecophysiological traits related to morphology, leaf gas exchange, water potential and structural traits were measured. Compared to control, S. suberba under dry conditions significantly decreased its aboveground biomass, photosynthetic rate (A), leaf water potential and nitrogen use efficiency, but increased intrinsic water use efficiency, root to shoot ratio and specific root length. S. superba under wet conditions also significantly decreased its total biomass, aboveground biomass and specific root length, while W had no effect on A and leaf water potential. Our results indicate that S. superba shows a decrease in carbon gain under drought stress, but less response under wet conditions. This emphasizes the need to consider the strength and frequency of rainfall pattern changes in future studies because rainfall may either alleviate or intensify the effects of drought stress depending on the moisture level, thus suitable water conditions is important for better management of this tree species in subtropical China.

Estimation of Coastal Wetland Vegetation Aboveground Biomass by Integrating UAV and Satellite Remote Sensing Data

Aboveground biomass (AGB) serves as a crucial indicator of the carbon sequestration capacity of coastal wetland ecosystems. Conducting extensive field surveys in coastal wetlands is both time-consuming and labor-intensive. Unmanned aerial vehicles (UAVs) and satellite remote sensing have been widely utilized to estimate regional AGB. However, the mixed pixel effects in satellite remote sensing hinder the precise estimation of AGB, while high-spatial resolution UAVs face challenges in estimating large-scale AGB. To fill this gap, this study proposed an integrated approach for estimating AGB using field sampling, a UAV, and Sentinel-2 satellite data. Firstly, based on multispectral data from the UAV, vegetation indices were computed and matched with field sampling data to develop the Field–UAV AGB estimation model, yielding AGB results at the UAV scale (1 m). Subsequently, these results were upscaled to the Sentinel-2 satellite scale (10 m). Vegetation indices from Sentinel-2 data were calculated and matched to establish the UAV–Satellite AGB model, enabling the estimation of AGB over large regional areas. Our findings revealed the AGB estimation model achieved an R2 value of 0.58 at the UAV scale and 0.74 at the satellite scale, significantly outperforming direct modeling from field data to satellite (R2 = −0.04). The AGB densities of the wetlands in Xieqian Bay, Meishan Bay, and Hangzhou Bay, Zhejiang Province, were 1440.27 g/m2, 1508.65 g/m2, and 1545.11 g/m2, respectively. The total AGB quantities were estimated to be 30,526.08 t, 34,219.97 t, and 296,382.91 t, respectively. This study underscores the potential of integrating UAV and satellite remote sensing for accurately assessing AGB in large coastal wetland regions, providing valuable support for the conservation and management of coastal wetland ecosystems.

Optimization of nitrogen and phosphorus fertilization for enhanced forage production and quality of Festuca Krylovianacv. Huanhu artificial grassland in alpine regions

Artificial grasslands of F. kryloviana in the region surrounding Qinghai Lake have been observed to a decline in productivity following three years of establishment. Traditional fertilization practices, aimed at maintaining ecological balance, have predominantly focused on the application of phosphorus. However, it remains unclear whether phosphorus fertilizers offer a superior advantage over nitrogen fertilizers in sustaining productivity. Consequently, from 2017 to 2019, we conducted an experimental to assess the impact of nitrogen and phosphorus fertilization on forage yield and quality. We designed with four levels of phosphorus and two levels of nitrogen, resulting in eight distinct fertilizer combinations. Our experimental findings indicate that the degradation of artificial grasslands leads to a shift in the allocation pattern of aboveground biomass. There was a respective decrease of 68.2 % and 62.5 % in the biomass proportions of stems and ears, contrasted by a greater than 200 % increase in the biomass proportion of leaves. The application of nitrogen not only elevated the total aboveground biomass but also promoted a preferential allocation of biomass to stems and leaves, consequently enhancing the forage's crude protein content. Nitrogen fertilization significantly increased aboveground biomass, and crude protein content by 63.21 %, and 6 %, respectively. Phosphorus fertilization's impact varied annually but favored the distribution of biomass to stems and ears. The net photosynthetic rate improved by over 53.12 % with fertilizer application, although the differences among treatments were not statistically significant. The balanced application of nitrogen and phosphorus fertilizers significantly bolstered the aboveground biomass, ear biomass, stem biomass, leaf biomass, and crude protein content in varying years by 17.25 %–209.83 %, 34.7 %–438.9 %, 25.5 %–250.2 %, 18.4 %–133.3 %, and 10.21 %–25.62 %, respectively. Our analysis revealed that nitrogen-only fertilization exhibited the most optimal fertilizer use efficiency and economic returns. In conclusion, nitrogen fertilization is crucial for sustaining the productivity and quality of F. kryloviana artificial grasslands. The local practice of 75 kg ha−1 phosphorus fertilizer is detrimental to the maintenance of productivity in F. kryloviana artificial grasslands. This study offers valuable insights into the optimization of fertilization strategies for sustainable forage production within alpine regions.

Biomass, carbon stock and carbon dioxide sequestration by trees outside forests: A case study from Puducherry, India

Trees outside forests (ToF) play a vital role in reducing carbon from industrial activities and vehicles by sequestering and storing atmospheric Co2 generated as biomass. However, there is a scarcity of studies quantifying the biomass and carbon stock in the ToFs. To bridge this gap, we conducted a study on the potential of biomass and carbon dioxide sequestration in trees planted in Puducherry. Our findings show that the total above-ground biomass of adult trees in the city was 1926.03 Megagram (Mg), while belowground biomass was 244.47 Mg. The total carbon stored in adult trees was 966.53 Mg, while the volume of sequestered CO2 was 3547.17 Mg in the study area. To increase carbon dioxide sequestration in Puducherry town, we recommend increasing urban green cover and planting more fast-growing native species.

Anthropogenic and climate impacts on carbon stocks of grassland ecosystems in Inner Mongolia and adjacent region

Up to date, most studies reported that degradation is worsened in the grassland ecosystems of Inner Mongolia and adjacent regions as a result of intensified grazing. This seems to be scientific when considering the total forage or total above-ground biomass as a degradation indicator, but it does not hold true in terms of soil organic carbon density (SOCD). In this study, we quantified the changes of grassland ecosystem carbon stock in Inner Mongolia and adjacent regions from the 1980s to 2000s and identified the major drivers influencing these variations, using the National Grassland Resource Inventory and Soil Survey Dataset in 1980s and the Inventory data during 2002 to 2009 covering 624 sampling plots concerned vegetal traits and edaphic properties across the study region. The result indicated that the above-, below-ground and total vegetation biomass declined from the 1980s to 2000s by ∼ 10 %. However, total forage production increased by 6.72 % when considering livestock intake. SOCD remained stable despite a 67 % increase in grazing intensity. A generalized linear model (GLIM) analysis suggested that an increase in grazing intensity from the 1980s to 2000s could only explain 1.04 % of the total biomass change, while changes in precipitation and temperature explained 17.7 % (p < 0.05) of total vegetation biomass (TVB) change. Meanwhile, SOCD change during 1980s - 2000s could be explained 10.08 % by the soil texture (p < 0.05) and <1.6 % by changes in climate and livestock. This implies that the impacts of climate change on grassland biomass are more significant than those of grazing utilization, and SOCD was resistant to both climate change and intensified grazing. Overall, intensified grazing did not result in significant negative impacts on the grassland carbon stocks in the study region during the 1980s and 2000s. The grassland ecosystems possess a mechanism to adjust their root-shoot ratio, enabling them to maintain resilience against grazing utilization.